Image‐guided metabolomics and transcriptomics reveal tumour heterogeneity in luminal A and B human breast cancer beyond glucose tracer uptake

Abstract Background Breast cancer is a metabolically heterogeneous disease, and although the concept of heterogeneous cancer metabolism is known, its precise role in human breast cancer is yet to be fully elucidated. Methods We investigated in an explorative approach a cohort of 42 primary mamma carcinoma patients with positron emission tomography/magnetic resonance imaging (PET/MR) prior to surgery, followed by histopathology and molecular diagnosis. From a subset of patients, which showed high metabolic heterogeneity based on tracer uptake and pathology classification, tumour centre and periphery specimen tissue samples were further investigated by a targeted breast cancer gene expression panel and quantitative metabolomics by nuclear magnetic resonance (NMR) spectroscopy. All data were analysed in a combinatory approach. Results [18F]FDG (2‐deoxy‐2‐[fluorine‐18]fluoro‐d‐glucose) tracer uptake confirmed dominance of glucose metabolism in the breast tumour centre, with lower levels in the periphery. Additionally, we observed differences in lipid and proliferation related genes between luminal A and B subtypes in the centre and periphery. Tumour periphery showed elevated acetate levels and enrichment in lipid metabolic pathways genes especially in luminal B. Furthermore, serine was increased in the periphery and higher expression of thymidylate synthase (TYMS) indicated one‐carbon metabolism increased in tumour periphery. The overall metabolic activity based on [18F]FDG uptake of luminal B subtype was higher than that of luminal A and the difference between the periphery and centre increased with tumour grade. Conclusion Our analysis indicates variations in metabolism among different breast cancer subtypes and sampling locations which details the heterogeneity of the breast tumours. Correlation analysis of [18F]FDG tracer uptake, transcriptome and tumour metabolites like acetate and serine facilitate the search for new candidates for metabolic tracers and permit distinguishing luminal A and B. This knowledge may help to differentiate subtypes preclinically or to provide patients guide for neoadjuvant therapy and optimised surgical protocols based on individual tumour metabolism.

• Central regions of LumA and LumB are dominated by glucose metabolism.
• In contrast, peripheral regions of LumA and LumB exhibit mostly lipid and one-carbon metabolism.
• Region-and tumour-specific metabolites may aid in new metabolic tracers development.
• Differences in central and peripheral [ 18 F]FDG glucose tracer uptake widen with tumour grade in LumA and LumB.

INTRODUCTION
Breast cancer is a heterogeneous disease where the tumour microenvironment (TME), genetic mutations and metabolic phenotypes play significant roles in its development. 1Global gene expression analyses have revealed the existence of at least five intrinsic subtypes of breast cancer (luminal A [LumA], luminal B [LumB], human epidermal growth factor receptor 2 [HER2]-enriched and basal-like), as well as a normal-like group. 2 These subtypes differ significantly in risk factors, incidence, baseline prognoses and responses to systemic therapies. 3Oestrogen receptor (ER) positive breast cancers are the most commonly occurring type and can be classified into two major molecular subtypes, LumA and LumB.Clinical manifestations, levels of malignancy, systematic therapeutic responses and survival outcomes can differ significantly between LumA and LumB despite similar histopathological features. 4Some molecular markers, such as progesterone receptor (PR), HER2 and Ki-67 are commonly used to distinguish between these two types.Furthermore, LumB tumours have a faster proliferation rate and lower expression of PRs than LumA tumours, which by contrast, are associated with a higher cumulative metastasis rate.Generally, cancer cells proliferate uncontrollably and have developed various strategies to maintain their proliferation rate high even under nutrient-limited conditions, known as metabolic plasticity. 5Gene mutations, such as those affecting p53, can significantly alter tumour metabolism, leading to a shift towards a more glycolytic phenotype characterised by increased aerobic glycolysis and decreased typical glucose metabolism. 6Mutations in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) activate the PI3K/AKT/mTOR pathway and are linked to increased glucose uptake, glycolysis and altered lipid metabolism. 7The high expression level of c-Myc promotes aerobic glycolysis and lactate production.It also influences the one-carbon unit synthesis pathway by up-regulating key enzymes like 3phosphoglycerate dehydrogenase, phosphoserine aminotransferase and phosphoserine phosphatase. 8,9Additionally, it drives glutamine metabolism by increasing the expression of glutamine transporters and glutaminase. 10arbohydrate metabolism-including aerobic glycolysis, pentose phosphate pathways, tricarboxylic acid cycle and gluconeogenesis -is a major source of energy for breast cancer cells and can be dysregulated through various channels and pathways, thereby promoting proliferation and survival. 11In recent years, dysfunctional lipid metabolism has increasingly been recognised as a hallmark of cancer. 12,13Clinical data indicate that postmenopausal women who are obese have a higher risk of developing breast cancer than lean women and is also associated with a poorer prognosis for women of all age groups. 14,15In lipid metabolism, de novo fatty acid (FA) synthesis, FA uptake and transport and FA oxidation (FAO) are up-regulated to meet the increased demand for biomass production. 16Furthermore, altered amino acid metabolism can be frequently observed in cancer.Amino acids serve as the primary building blocks of proteins, but can also regulate metabolites that support the growth of breast cancer cells. 17tilising techniques like radiographic imaging and genomics, we can observe tumour heterogeneity, and sequencing biopsied tissues from various regions uncovers clonal amplification, 18,19 underscoring the challenge in cancer prevention and treatment.The uneven distribution of vasculature among tissues poses difficulties for effective therapeutic interventions.The TME, hosting diverse cell types, plays a pivotal role in shaping the unique metabolic landscape. 20Non-tumour cells, including stromal and immune cells, actively contribute to the extracellular matrix, influencing tumour growth.Regions with varying immune cell presence may undergo swift mutations, exploring alternative metabolic pathways to evade immune clearance.By categorising tumour tissues into central and peripheral based on glucose tracer uptake differences, we aim to spatially classify tissues, providing a comprehensive understanding of metabolic changes in breast tumour regions.
Recent research on breast cancer has highlighted the variations in metabolism among different subtypes of the disease. 21,22These metabolic differences have been shown to play a crucial role in tumour growth and progression, and understanding them is essential for developing effective treatments for breast cancer.ERs herein play a central role in metabolic regulation by interacting with various cellular key regulators, including hypoxia-inducible factor, Ras/Raf/MAPK, PI3K/Akt/mTOR, p53 and c-MYC. 23hile HR status and HER2 status are commonly used to guide therapy and treatment, the documentation on how to make effective treatment choices based on tumour subtypes is currently limited. 24Even though pathological markers (ER, PR and HER2) can be used to differentiate between LumA and LumB breast cancers, and these markers are routinely used clinically to stratify patients for prediction and treatment selection, a preclinical approach to differentiate between these two types and for subsequent treatment selection is lacking.Therefore, in an explorative approach we collected central and peripheral tumour tissues from LumA and LumB patients for metabolomic and transcriptomic comparison with glucose metabolism assessed employing positron emission tomography/magnetic resonance imaging (PET/MR), which facilitated our understanding of the biological heterogeneity of these two subtypes.This was done in an interdisciplinary research approach to link in vivo imaging with ex vivo molecular diagnostics, including advanced omics techniques to decipher tumour heterogeneity in human breast carcinoma.

Ethical background
This study was approved by the ethics committee, Faculty of Medicine, University of Tübingen, Germany (516/2016BO1, study number GK-PET/MR Tü018).All patients gave the written informed consent according the declaration of Helsinki.Sampling did not influence patient treatment.All data were pseudonymised according to European Data Protection Regulations and German law.

Collection and storage of patient specimens
All female patients with a first primary diagnosis of invasive breast cancer underwent surgery to remove the tumour.Tumour samples were collected from patients undergoing surgery at the Women's Hospital, University Hospital Tübingen, Germany.Before surgery, MRI and PET/MR images were reviewed in a joint session with the designated study radiologist (H.P.) and study pathologist (A.S.).Areas for sampling the fresh tissue were agreed on, with a preference for high-uptake regions in the tumour periphery and a representative area in the tumour centre.Optimal sampling regions were documented in a standardised graph including sagittal and horizontal views of the breast.In the operating room, the resection specimen was completely put in an ice box and brought immediately to the pathology laboratory.The pathologist rapidly oriented the specimen according to the sampling map and performed the differential inking of the specimen according to four directions (ventral, dorsal, cranial and caudal).Serial sectioning was performed in 5-8 mm intervals along the mammilla-peripheral longitudinal axis of the segmentectomy or in sagittal silences of the mastectomy.The slices were laid out on in cold plate at −20 • , which was covered by a single layer of cellulose (to prevent tissue from sticking to the metal).Regions of the tumour corresponding to the regions of interest were identified.Samples were taken with a sterile 3 mm single use punch biopsy from the centre and periphery.If tumours were too small for punching, the whole tumour was frozen.The tissue cores were placed in cryo-tubes, snap frozen and then stored at −80 • C until analysis.Areas immediately surrounding the collection site were formalin fixed and embedded in paraffin blocks for further analysis by light microscopy, immunohistochemistry (IHC) and gene expression profiling by Nanostring PAM50.Patient information was collected, including standard demographics, cancer histology and the extent of peritoneal disease.Samples and data were pseudonymised.Of note, no tumour centre tissue with necrotic parts was used for this study.

Clinical histopathology
Samples were fixed in 10% neutral formalin for 24 h.Nodal and tumour samples were obtained by routine pathological techniques, such as haematoxylin and eosin (H&E) staining.Experienced pathologists evaluated all histopathological features, such as tumour size, histopathological type and grade.Histopathologic types were classified according to the 2012 WHO classifications.Histologic grades were assigned according to Elston and Ellis. 25he characterisation of HR and HER2 status was conducted on biopsy specimens prior to any treatment.The ASCO/CAP guidelines available during diagnosis were used to determine HER2 status. 26HER2-low tumours were identified by IHC as 1+ or IHC 2+/ISH non-amplified, while HER2-0 was defined as IHC 0. The HR status was determined based on the currently available IHC data: tumours were classified as luminal-like if the ER and/or PR levels were ≥1% or triple-negative breast cancer if the ER and PR levels were both <1%.Additionally, the tumours were assigned a histologic grade according to the Nottingham histologic scoring system. 25ll patients were classified into molecular subtypes based on Nanostring PAM 50 gene expression data performed on tumour RNA of the formalin fixed and paraffin embedded resection specimen.The data were confirmed by correlation with IHC for ER and PR (positive when > 1%) and Ki67 (>20% in LumB).In addition, HER2 analysis was performed, consisting of a combination of IHC and fluorescent in situ hybridisation (FISH) for HER2 gene amplification if necessary (IHC unequivocally positive with score 3+, additional FISH performed when IHC score intermediate with score 2+).

TCGA databases analysis
The mRNA expression comparison with LumA and LumB was done by downloading respective data sets from The Cancer Genome Atlas (TCGA) database.The intrinsic subtypes of breast cancer, defined by differential expression of 50 genes called PAM50, including basal-like, LumA, LumB, Her2 and normal-like subtypes. 27The PAM50 signature of TCGA database was downloaded from the cancer immunome atlas (https://tcia.at/).The basal-like (n = 218), Her2 (n = 131), LumA (n = 302), LumB (n = 267) and normal-like (n = 126) molecular subtypes were compared.Principal component analysis (PCA) was applied to visualise breast cancer subtypes utilising the 'limma' and 'ggplot' R language packages.The survival analysis was calculated through the Kaplan-Meier curve.The gene set variation analysis (GSVA) package was used for singlesample gene set enrichment analysis (ssGSEA) analysis to obtain a Hallmark gene set score.The MutationalPatterns R package was used to analyse mutational signatures in the targeted sequencing data, providing a diverse set of tools for assessing transcriptional and replicative strand bias, genomic distribution and association with genomic features.size 0.9 × 0.9 × 0.9 mm 3 ) were used.ADC maps were calculated from the diffusion-weighted (DW) echo planar imaging (EPI).

PET/MR imaging
For whole body PET/MR examination the following MR settings were performed: a transversal and coronal T2weighted turbo spin echo sequence, a coronal whole body STIR sequence in free breathing, whole body diffusion weighted imaging, whole body T1-weighted volumetric interpolated breath-hold examination sequence after intravenous injection of 0.1 mmol/kg gadolinium-based MRI contrast media (Gadovist R ; Bayer Vital GmbH, Leverkusen, Germany), a fluid attenuated inversion recovery sequence of the head as well as a contrast-enhanced T1weighted 3-D magnetisation prepared rapid gradient echo sequence of the head.
The injected dose of [ 18 F]FDG patients received was adjusted to the patient body weight (average: 2.5 ± 0.60 MBq/kg).Data were acquired on a state-of-the-art PET/MR scanner (Biograph mMR; Siemens Healthineers, Erlangen, Germany).

RNA isolation and gene expression assay
H&E-stained sections of formalin-fixed paraffinembedded (FFPE) tumour tissues were examined, and regions comprising representative invasive breast carcinoma were delineated on each slide.The tumour centre and periphery were defined using the outlined regions, and 10 μm thick sections were cut and macro dissected to eliminate surrounding normal tissue beyond the delineated area.
According to the manufacturer's instructions, RNA was extracted from macro-dissected 5 μm paraffin sections using the Maxwell R RSC RNA FFPE Kit and the Maxwell R RSC Instrument (Promega, Madison, WI, USA).The RNA samples were evaluated for yield and purity using a NanoDrop 2000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).RNA quality and DV200 values were assessed using an Agilent Fragment Analyzer Instrument for quality control (QC) purposes.Samples that did not pass the analysis or QC were removed, resulting in a final set of nine LumA and 15 LumB tumour central and peripheral tissues that met the quality standards established by NanoString.
The NanoString Breast Cancer 360 assay (BC360TM) was used to measure gene expression on a NanoString nCounter R SPRINT Profiler (NanoString Technologies Inc., Seattle, WA, USA), including 758 gene-specific probe pairs of the BC360 targets, 18 housekeeping genes used for normalisation, nine exogenous positive control RNA targets, and eight exogeneous negative control sequences.The BC360TM gene expression panel was incubated overnight in a solution containing 50-250 ng of total RNA at 65 • C to enable hybridisation.

Transcriptomics analysis
The NanoString ROSALIND platform was employed for nCounter analysis, and nCounts of mRNA transcripts were normalised using the geometric means of 18 housekeeping genes (ABCF1, ACTB, G6PD, GUSB, MRPL19, NRDE2, OAZ1, POLR2A, PSMC4, PUM1, RPLR2A, SDHA, SF3A1, STK11IP, TBC1D10B, TBP, TFRC and UBB).Housekeeping probes for normalisation are selected based on the geNorm algorithm implemented in the NormqPCR R library1.The PCA was used for visualising the clusters of LumA and LumB centre and periphery samples.The GSVA package was used for ssGSEA analysis to obtain a Hallmark gene set score.The average score of hallmark pathway GSVA score for each subtype was shown by balloon plot using ggpubr package.In addition, the GSEA, 29 the statistical significance was defined as NES > 1, p < .05,and the overrepresentation of indicated gene ontology (GO) biology process (BP) gene sets in the ranked gene lists presented by the normalised enrichment score (NES).Out of the 758 gene-specific covered by the BC360 panel, 454 genes aligned with the Hallmarks gene sets.Additionally, 527 genes were associated with metabolism-related GO BP gene sets.

Tissue metabolite extraction for metabolomics analysis
The LumA centre (n = 10) and periphery (n = 10), LumB centre (n = 8) and periphery (n = 8) tumour tissues were cryogenically pulverised using liquid nitrogen with the Covaris tube (Covaris, Woburn, MA, USA) and then transferred into adaptive focused acoustics-compatible glass tubes.A two-phase metabolite extraction protocol was implemented to avoid lipid signal overlap in the spectra.For total lipid extraction, 300 μL of methanol and 1000 μL of tert-butyl methyl ether were added to the Covaris tube using filter-containing pipette tips and vortexed until a homogeneous solution was obtained.Each sample was subjected to a 5-minute ultrasound extraction using the ultrasonicator (E220 Evolution instrument; Covaris).Two hundred and fifty microliters of ultrapure water was added to achieve two-layer phase separation.The polar phase was separated into HPLC glass vials tubes, evaporated to dryness using a speedvac (Thermo Scientific Savant SPD, Waltham, MA, USA) and used for the subsequent metabolomics analysis.

Metabolomics analysis of polar metabolites by 1 H-NMR spectrometry
The dry metabolite pellets were re-suspended in a deuterated phosphate buffer adjusted to a pH of 7.4, containing 1 mM 3-(trimethylsilyl) propionic-2,2,3,3-d 4 acid sodium salt as an internal standard (Sigma-Aldrich Chemie, Taufkirchen, Germany).The supernatants were pipetted to a 1.7 mm nuclear magnetic resonance (NMR) spectrometer-compatible tube (Bruker Biospin, Rheinstetten, Germany) and measured with a triple resonance 1.7 mm room temperature probe.The NMR spectra of individual samples at 298 K were recorded on a 14.1 Tesla ultra shielded NMR spectrometer (600 MHz proton frequency, Avance III HD; Bruker BioSpin, Ettlingen, Germany).The polar sample spectra were recorded using 1D nuclear Overhauser effect spectroscopy (NOESY) and Carr-Purcell-Meiboom-Gill (CPMG) experiments.The 90 • excitation pulse P1, for NOESYs, ranged from 5.68 to 6.11 μs, and for CPMGs, the range from 5.68 to 6.05 μs.The relaxation delay (D1) was uniformly set at 4.0 s for both NOESYs and CPMGs.The acquisition time for each NOESY and CPMG scan was set to AQ = 2.73 s.The number of acquisitions (NS) varied for different samples; for NOESYs it was 64 scans per sample, resulting in 58 min 48 s per analysis while for CPMGs, the number of scans ranged from 512 to 4096 (corresponding a maximum duration of 7 h 47 min 19 s) depending on signal to noise ratio and laboratory estimates of the dilution factor of samples.Receiver gain value was set to 84.54 and time domain to 65 536 data points.The spectral preprocessing was performed using Bruker TopSpin 3.6.1,and the metabolite assignment and quantification were carried out using ChenomX NMR Suite 8.5 software and the CPMG spectra.

Chemometrics and statistics
Metabolite concentrations of each sample were exported into a data matrix.The probabilistic quotient normalisation approach was used to normalise the data for dilution effects, with a reference sample serving as the basis for normalisation.Univariate, multivariate and correlation analysis was performed using the MetaboAnalyst 5.0 online platform. 30The paired t-test was used for statistical comparison as the breast cancer tissue was divided into central and peripheral tissues from the same patient.The volcano plot was used to analyse the metabolites present in LumA and LumB, with a significant value of (pair ttest) raw p values < .05 and fold change (FC) cut-off > 1.

The consort diagram of samples collection
Figure 1 illustrates the samples and analysis types performed within this study.From a total of 61 patients that gave consent to participate in the study, 60 patients were examined by [ 18 F]FDG-PET/MR and out of those 42 deeply phenotype by transcriptomics and metabolomics analysis, respectively, focusing on LumA (n = 24) and LumB (n = 18).

Clinical information of patients
Table 1 illustrates the subtypes according to the IHC results, location of tumour and clinic-pathological characteristics of the patients.

PET/MR imaging and NanoString results in central and periphery tumour
Forty-two patients with LumA and LumB breast cancer were recruited for PET/MR and metabolic changes in central and peripheral tumours areas were detected by measuring [ 18 F]FDG uptake values.Detailed imagine data of the patients were presented in Table 2.The [ 18 F]FDG SUV mean was significantly higher in the periphery than in the centre for LumA and LumB.The [ 18 F]FDG SUV mean was higher in the centre and periphery of LumB than in LumA.
The box plot in Figure 3 illustrates significantly higher mean standardised uptake values (SUV) of [ 18 F]FDG in tumour centres than in the periphery (Figure 3A), and the SUV mean of LumB was higher than that of LumA in both the centre and the periphery (Table 2).The difference between the peripheral and central SUV means of LumA and LumB both increased with tumour grade (Figure 3B).
An overview of signature pathways based on the average GSVA scores of the LumA and LumB samples shows that the FA metabolism genome scores higher in these pathways (Figure 3C).According to the PCA plot, there was no noticeable distinction in gene expression between the tumour centre and periphery.However, there was a clear differentiation between the LumA and LumB subtypes (Figure 3D).The scree plot shows the percentage of explained variances by the first ten principal components (Figure S1B).GSEA showed that nine of the metabolismrelated GO bioprocesses (BPs) were significantly enriched in LumA centre (Table S1), and 26 of the metabolismrelated GO BPs were significantly enriched in LumB centre and 3 BPs were significantly enriched in periphery (Table S2).The enrichment scores of lipid metabolism-related pathways in peripheral tumours were higher in LumB than in LumA (Figure S5).In LumA, the five metabolismrelated GO BP with the highest enrichment scores at the periphery compared with centre were phosphatidylcholine metabolic process (NES = 1.58, p = .002),FA metabolic process (NES = 1.46, p = .02),cellular ketone metabolic process (NES = 1.42, p = .03),cellular lipid metabolic process (NES = 1.42, p = .004)and regulation of cellular ketone metabolic process (NES = 1.40, p = .044)(Figure 2E).The five metabolism-related GO BPs with the highest enrichment scores at the periphery compared with the centre in LumB were FA metabolic process (NES = 1.79, p = .000),aminoglycan metabolic process (NES = 1.75, p = .001),cellular lipid metabolic process (NES = 1.72, p = .000),monocarboxylic acid metabolic process (NES = 1.72, p = .000)and organic acid metabolic process (NES = 1.69, p = .003)(Figure 2F).The catabolic process of glucose was enriched in the central tumour of LumA (NES = −1.42,p = .08)and LumB (NES = −1.06,p = .36)(Figure S1), further validating the PET/MRI results that the tumour centre was dominated by glucose metabolism.The KEGG (Figure S3A) and RECOME (Figure S3B) pathways in LumA and LumB were also performed (NES > 1, p < .05).

Metabolic differences between tumour centre and periphery in different tumour subtypes
The oPLS-DA score plot shows that the metabolic phenotype of LumA or LumB tumours is parallel in the centre and periphery (Figures 5A and B).Herein, acetate, formate and lactate were discriminated concerning other parameters in the PCA biplot of LumA and LumB (Figures 5C andD).The scree plot shows the percentage of explained variances by the first ten principal components of LumA and LumB (Figures S3A and B).In the volcano plot based on the FC > 1.2, p < .05(pair t-test), we identified that glycerol, glutathione disulfide (GSSG) and ethanolamine were increased, while lactate, O-phosphoethanolamine, myo-inositol and tyrosine were decreased in the periphery of LumA tumour (Figure 4E).In the periphery of LumB tumour, glucose and GSSG were up-regulated, while lactate and O-phosphoethanolamine were decreased (Figure 5F).To distinguish the most important metabolites between the centre and periphery, variable importance in projection (VIP) scores were used to screen for differential metabolites.Herein, lactate, glycerol, maltose, isoleucine, creatine phosphate, valine, citrate, glutathione, creatine, 2-hydroxybutyrate, methionine, ATP, formate, O-phosphocholine, alanine and O-phosphoethanolamine showed a VIP score greater than 1 in LumA (Figure 5G).Ethanolamine, serine, valine, betaine, GSSG, choline, O-phosphoethanolamine, lactate, succinate, glutamine, methionine, aspartate, creatine, alanine, ATP, tyrosine, leucine and glutathione PLS-DA VIP score were more than 1 in LumB (Figure 5H).The deviation diagram showed peripheral concentrations compared with central concentrations for metabolites with FC > 1.2.A clear distinction could be observed between the peripheral and central metabolism in LumA and LumB.LumB has generally more differentiating metabolites than LumA.Glycerol concentration was higher only in peripheral tumours of LumA (Figure 5I).By contrast, creatine, alanine, valine and glycine concentrations were found to be elevated only in central tumours of LumB, while 2-hydroxybutyrate, fumarate, serine, glucose and acetate concentrations were increased exclusively in peripheral tumours of LumB (Figure 5J).The heatmaps and correlation heatmaps of metabolites in the peripheral and central tumours of LumA and LumB are shown in Supplementary Figure S2.

Correlation of multi-omics results
The Spearman correlation method was employed to determine the correlation between the mean values of [ 18 F]FDG uptake, GSVA scores and difference genes with metabolite concentrations.The correlation between the mean SUV of [ 18 F]FDG and metabolites was different in LumA and LumB.The correlations between [ 18 F]FDG tracer uptake and metabolites, including 3hydroxybutyrate, aspartate, betaine, choline, citrate, creatine phosphate, ethanolamine, GSSG, glucose, lysine, acid metabolism pathway were higher than glycolysis pathway.(D) Principal component analysis (PCA) plot: gene expression profiles in both the tumour centre and periphery were indistinguishable, but was distinct between LumA and LumB subtypes.(E) Gene set enrichment analysis (GSEA) analysis: the top five metabolic gene ontology (GO) biological process (BP) enrichment in tumour periphery compare with centre in LumA, all five processes were related to lipid metabolism-related process (F) GSEA analysis: the top five metabolic pathway enrichment in tumour periphery compare with centre in LumB, which related to lipid and amino metabolism-related processes (*p < .05,**p < .01,***p < .001).O-acetylcarnitine, serine, succinate, taurine and snglycero-3-phosphocholine were reverse in LumA and LumB (Figure 6A).We next conducted correlation analysis of metabolites with GSVA scores from the GO FA metabolism process gene set, with the aim of identifying metabolites that may be associated with lipid metabolism.Herein, peripheral LumB FA metabolism showed positive correlations (R > .5)with 3-hydroxybutyrate, acetate, citrate and maltose; and negative correlations (R< -.05) with betaine, glutamine, histidine, isoleucine, lactate, methionine, proline, tyrosine and sn-glycero-3-phosphocholine (Figure 6B).Genes associated with lipid metabolism were selected from those genes statistically different at the periphery and centre of LumA and LumB.

Metabolic phenotypes in the tumour centre and periphery of LumA and LumB
We extensively analysed metabolic pathways and their association with gene expression levels to capture the metabolic heterogeneity between central and peripheral regions of breast tumours.In the metabolomics data set, the typical patterns of the Warburg effect were observed in the tumour centre and characterised by high lactate levels and low glucose levels.Cyclin-dependent kinase 1 (CDK1), E2F transcription factor 1 (E2F1) and PTEN, all linked to the Warburg effect, exhibited significantly elevated expression levels in the periphery of LumA.Moreover, PDK4, EGFR, PIK3CA and Phosphoinositide-3-Kinase Regulatory Subunit 5 (PIK3R5), associated with the Warburg effect, displayed notable expression up-regulation in the periphery of LumB (Figure S10A).Additionally, we identified the presence of the Kennedy pathway, known as cellular membrane growth metabolism, based on the high levels of O-phosphoethanolamine and phosphocholine in the tumour centre, and high levels of ethanolamine and choline in the tumour periphery.The gene PLA2G2A, involved in the Kennedy pathway, exhibited a significant up-regulation in expression in the periphery of LumA (Figure S10C).Furthermore, one-carbon metabolism was identified with high glycine in tumour centre and high serine in tumour periphery.TYMS, a key gene in the one-carbon metabolic process, also showed significant upregulation in expression in the periphery of LumA (Figure S10B).In addition, we observed increased levels of acetate and glycerol in the tumour periphery.Notably, the expression of CD36, a receptor with a pivotal role in lipid uptake and metabolism, and LPL, closely associated with triglyceride catabolism, exhibited significant up-regulation in the periphery of LumB (Figure S10D).These findings collectively suggest that lipolysis is heightened in the peripheral mammary lipid tissue, contributing to energy regeneration within the tumours even with absence of glucose.For a visual summary of the metabolites and related gene between the tumour centre and periphery: red plots mean up regular in periphery, and blue mean up regular in centre (p < .05,pair t-test).expression in central and peripheral tumour regions, we produced a graphical abstract to present it (Figure 7).
Due to the potential impact of varying proportions of stromal and immune cells on metabolic heterogeneity within the TME, we employed the ESTIMATE method.This tool assesses tumour purity and evaluates the presence of stromal and immune cells based on gene expression data.Despite the significance of this analysis, our findings revealed that the ESTIMATE algorithm's stromal score (indicating the presence of stromal cells), immune fraction (indicating the presence of immune cells) and overall ESTIMATE score (reflecting overall tumour purity) did not exhibit notable differences between the central and peripheral regions of LumA and LumB tumours (Figure S11).

DISCUSSION
4][35] In our study, we harnessed a multiomics approach to unveil the heterogeneity of tumours in primary human breast cancer and establish correlations with in vivo imaging tracer uptake studies.We confirmed the predominance of glucose metabolism in the centre of breast tumours, while lipid metabolism was more prominent in the periphery of the tumours.Furthermore, the overall metabolic activity based on glucose tracer uptake of LumB was higher than that of LumA.These results will aid the understanding of the metabolic heterogeneity in LumA and LumB subtypes, and correlation analysis of in vivo imaging and ex vivo molecular diagnostics could help the search for new candidates for metabolic tracers, enabling the differentiation between LumA and LumB subtypes using pre-surgical tests, and could thus stratify patients for neoadjuvant therapy or optimised surgical protocols.

Metabolic heterogeneity in breast cancer
Increasing evidence shows that cancer cells exhibit heterogeneous metabolic requirements and preferences. 36,37derstanding the emergence and evolution of metabolic heterogeneity in cancer is critical because it impacts our approach to utilise metabolic reprogramming for both cancer diagnostics and treatment.Herein, the metabolic adaptations of cancer are influenced by a range of intrinsic and extrinsic factors, which can significantly impact on tumour growth and progression, much like genomic or immune alterations.Intrinsic factors include genetic mutations or alterations in oncogenes or tumour suppressor genes, while extrinsic factors include changes in the TME, such as nutrient availability, oxygen levels and pH. 38,39he major metabolic adaptation in cancer, known as the Warburg effect or aerobic glycolysis, provides cancer cells with the energy and building blocks needed for rapid proliferation. 40This study found that breast tumours exhibited a higher glucose uptake rate in their central region, accompanied by decreased glucose concentration and increased lactate.These findings thus suggest that cancer cells in the centre of the tumour rely heavily on glycolysis, a metabolic adaptation that may promote tumour progression, and they highlight the importance of understanding the metabolic adaptations of cancer cells in different regions of tumours.
In the periphery of tumours, we found different alterations: Metabolic differences between the centre and periphery of breast tumours may be related to the TME.Other researchers have observed that [ 18 F]FDG uptake positively correlated with hypoxia and negatively correlated with cellular proliferation and tumour blood flow. 41,42This is in line with our research that [ 18 F]FDG uptake was higher in the central region, indicating more hypoxia and likely a lower proliferation.Furthermore, our research shows vascular endothelial growth factor receptor 2 (VEGFR2) was increased in the periphery, especially in LumB.CD34 Molecule (CD34) is a valuable vascular marker used in identifying and characterising endothelial cells, particularly those involved in angiogenesis and vascular biology, which was found to be negatively correlated with [ 18 F]FDG uptake (Figure S11).This evidence may prove tumour vasculature may be enriched in the periphery.On the other hand, dysregulation in lipid metabolism is among the most prominent metabolic alterations to obtain alternative energy substrates, building blocks for biological membranes, and signalling molecules needed for proliferation, survival, invasion and metastasis. 43,44rrelation network: (C and D) lipid-related statistically different genes associated with metabolites.Different genes are represented by yellow triangles, while metabolites are illustrated by green circles.The distance between the forms indicates the degree of closeness of the relationship.Red lines indicate positive correlation, while blue lines demonstrate negative correlation (R > .5,FDR < .05).There are more lipid metabolism genes correlated with metabolites in LumB than in LumA.(E) Serine-related genes and their association with other one-carbon unit metabolites (R > .45,FDR < .1).Studies have consistently observed the aberrant choline phospholipid metabolism in breast cancer cells, highlighting a robust correlation with malignant progression. 45urthermore, elevated de novo FA synthesis is imperative for the sustained proliferation of tumour cells, ensuring a constant supply of lipids, including phospholipids, for membrane synthesis. 46In our results, gene expression in the periphery is enriched in cellular lipid metabolic processes, phosphatidylcholine metabolic processes and FA metabolic processes.
Furthermore, in cancer, it is commonly observed that there is an increase in the levels of phosphoethanolamine, and an increase in phosphocholine, which is indicative of enhanced cell proliferation. 47The Kennedy pathway is the major route for forming of ethanolamine-derived phospholipids, essential structural components of the cell membranes, known as cellular membrane growth metabolism. 48The accumulation can disturb the Kennedy pathway, impairing membrane function and signaling. 49his study found that higher ethanolamine and choline in the periphery and higher O-phosphoethanolamine and phosphocholine in the centre, especially in LumB.
In one-carbon metabolism, serine derived from glycolysis and exogenous uptake can be converted to glycine, providing the one-carbon unit for one-carbon metabolism. 50This metabolism can offer many intermediate metabolites as central precursors for synthesising of proteins, lipids and nucleic acids, forming a complex metabolic network for tumour progression. 51Serine is a major donor of one-carbon units to the folate cycle through one-carbon metabolism while producing glycine. 52Adenosine, guanosine and thymidylate are de novo synthesised in the folate cycle, which is necessary for the synthesis of nicotinamide adenine dinucleotide, nicotinamide adenine dinucleotide phosphate and ATP in mitochondria. 53The folate cycle is linked to the methionine cycle, which produces methyl groups that contribute to cellular biosynthesis and posttranslational modifications. 54The methionine cycle also provides precursors, such as cysteine for glutathione synthesis, which is essential for redox buffering. 55Thus, de novo serine metabolism may be required and adequate for tumour maintenance and promotion of oncogenesis. 9Our study indicates that serine increased in the periphery and glycine increased in the centre.Moreover, we found that some genes correlated with serine in breast cancer.The characteristic Treg transcription factors Foxp3 and Treg function are dramatically lost under various inflammatory conditions. 56Tregs with aberrant metabolism show increased serine metabolism but down-regulation of FOXP3. 57In our results, FOXP3 was negatively correlated with serine, therefore it is pos-sible that serine can affect the TME, resulting in tumour proliferation and progression.GDF15 has been reported to influence folate metabolism, a key component of onecarbon metabolism, 58 it was also negatively correlated with serine.
LumA and LumB peripheral genes were significantly enriched in lipid metabolism pathways.Acetate and CD36 were significantly elevated in peripheral tumours of LumB in our results.While the reprogramming of glucose metabolism was the first recognised metabolic abnormality in tumour cells, there is increasing attention being given to the metabolic reprogramming of lipids in cancer, 59,60 especially for tumours that grow in a lipid-rich environment, such as breast cancer.During cell proliferation, activated FA synthesis meets the demand for rapid membrane generation, while FAO provides the energy required for vigorous growth. 61During breast tumour invasion into surrounding normal tissues, tumour cells frequently encounter metabolic stress, such as hypoxia and nutrient deprivation, and therefore must absorb FAs and store lipids to generate energy for survival. 62Breast cancer cells demonstrate de novo FA synthesis, with increased expression of unsaturated FAs. 63Furthermore, FAO is more active in receptor-positive breast cancers, similar to de novo FA synthesis. 22In breast cancer tissue, a significant proportion of the mesenchyme is occupied by cancerassociated adipocytes, indicating that adipocytes play a substantial role in the TME. 64CD36, known as FA translocase, is also a crucial enzyme involved in the uptake of FAs, and evidence suggests that it contribute to breast cancer progression and up-regulated in tumour cells and is responsible for the uptake of exogenous FAs into cell membranes. 65

Crosstalk between metabolism and genes
Emerging evidence suggests that the activation of oncogenic pathways can up-regulate specific metabolic pathways in cancer cells.In this study, we observed that cases of LumB breast cancer exhibited up-regulation of oncogenic pathways that are closely associated with glycolysis, including the PI3K.The PI3K/AKT/mTOR pathway is frequently altered in luminal-type tumours, with 40−50% of cases exhibiting mutations in pathway elements such as PIK3CA, PIK3R1, PTEN and AKT1. 66Among these genes, PIK3CA and MAP3K1 are the most commonly mutated in this subtype. 67The TCGA database analysis we conducted produced identical results.
BIRC5, CCNE1, CDK2, TYMS and MKI67 are the five most significant differential genes in LumA types.These genes significantly affect role in breast cancer development and progression, particularly in cell cycle regulation and proliferation.BIRC5 is a human gene that encodes a protein critical in preventing apoptosis. 68CCNE1 is a human gene that encodes a protein critical role in cell cycle regulation. 69The protein is a regulatory subunit of cyclin-dependent kinase 2 (CDK2), a protein kinase that promotes cell cycle progression. 70TYMS is a human gene that encodes an enzyme that plays a key role in DNA synthesis by catalysing the conversion of deoxyuridine monophosphate to deoxythymidine monophosphate (dTMP). 71dTMP is a precursor of thymidine, an essential building block for DNA replication and repair.Ki-67 (MKI67) is a protein that is commonly used as a marker to measure the proliferation rate of cells and determine the grade of certain tumours, detected through IHC staining. 72he top five differential genes in LumB tumours, ranked by significance, are MIS18A, WIF1, CHEK2, EMCN and CDK4.MIS18A is involved in chromosome segregation during cell division by maintaining the centromeres, 73 whereas WIF1 regulates the Wnt signalling pathway, 74 which is essential for various cellular processes.CHEK2 plays a crucial role in DNA damage response and cell cycle regulation, and mutations in this gene can increase cancer risk. 75EMCN regulates the permeability of blood vessels and may have implications for inflammation and tumour growth. 76CDK4 is an enzyme that works with cyclin D1 to promote cell cycle progression by activating Rb, and overexpression of CDK4 has been linked to certain types of breast cancer, as well as other cancers. 77ur results positively correlated GRIA3, MUC1 and LRP2 with serine in our results.GRIA3 is a receptor of glutamate involved in the glutamate signalling pathway, and glutamate is interconnected with one-carbon metabolism through the generation of α-ketoglutarate. 78MUC1 regulates carbon flux by directly modulating metabolic enzymes like PKM2, 79 which may help accumulate precursor substances for one-carbon metabolism. 80,81LRP2 is involved in the internalisation and transport of folatebinding proteins, such as folate receptor alpha (FOLR1), which is responsible for cellular uptake of folate, which is an essential component of one-carbon metabolism. 82he key aspect of our study was dividing the tumours into peripheral and central sites and analysing the genetic variations.Although the initial differential genes identified are primarily involved in regulating the cell cycle and cell proliferation, they are not directly involved in metabolism.In general, the enrichment of lipid metabolism pathways in tumour periphery samples compared with tumour centre samples was observed for both LumA and LumB breast cancer.

4.3
Correlation of glucose uptake value and FA metabolism score with metabolites Evidence suggests that ketosis, 3-hydroxybutyrate (3-HB), may significantly slow cancer progression in preclinical cancer models and patients. 83,84A previous study has reported that exposing breast cancer cells to 3-HB increased in glycolysis. 85According to our study, 3-HB levels were higher in the centre of LumB breast tumours but lower in the periphery.Additionally, there was a positive correlation between 3-HB levels and glucose uptake rate in LumB subtypes, while it was negatively correlated in LumA tumours.One possibility is that LumA and LumB tumours have distinct metabolic phenotypes, LumA typically has lower metabolic activity, while LumB have higher metabolic activity and rely more on glycolysis. 86cetate and glycerol showed positively correlated with the GSVA score of the FA metabolism in LumB, especially in the periphery tumour.However, in LumA, this relationship was found to be the opposite.This could be attribute to the distinct molecular and metabolic profiles of LumB and LumA.Specifically, LumB tumours have been shown to have higher levels of lipid metabolism and exhibit a more aggressive phenotype than LumA.To satisfy the additional energy requirements, FA synthesis is a necessary metabolic change among all the reprogramming of metabolism, especially in the case of low cellular glucose uptake in which the regeneration of acetyl CoA from citrate is restrained.
FOS positively correlated with acetate and has been shown to regulate the expression of acetyl-CoA synthetase (ACSS), an enzyme that catalyses the conversion of acetate to acetyl-CoA and a key intermediate in lipid synthesis. 87,88ID4 positively correlated with acetate and was up-regulated in the LumB periphery (Table S3).ID4 has been shown to regulate adipocyte differentiation involved in FA synthesis. 89ID4 is important for both mammary gland development and also for the etiologic of breast cancer. 90,91ID4 is overexpressed in a subset of breast cancer patients, marking patients with poor survival outcomes. 92ccording to our findings, ID4 can act as a transcriptional coactivator of the Kennedy pathway.Some studies have suggested that elevated levels of total choline and phosphocholine (PC) are consistently observed in aggressive forms of cancer. 93,94Choline kinase-α is frequently overexpressed in various types of cancers and is closely associated with tumour progression and invasiveness. 95Moreover, tumour proliferation involves changes in the composition of choline-containing metabolites, characterised by elevated levels of choline and its phosphorylated metabolites in individuals with tumours. 94However, more research is needed to fully understand the relationship of the Kennedy pathway together with ID4 in breast cancer.
Towards the development of novel PET tracers, numerous publications focus on the potential of 11 C-acetate as a PET tracer in oncology. 96,97A clinical trial has reported that combining PET examinations with FDG and 11 Cacetate provides added value in the diagnosis of hepatocellular carcinoma compared with single-tracer imaging. 98he combination of 11 C-acetate and [ 18 F]FDG may help to realise precise diagnosis avoiding false negative results and influencing the treatment methods depending on the final stage.The inverse correlation between acetate and serine in LumA and LumB subtypes holds promise as a nuclear medicine evidence to elucidate the distinction between these two subtypes in preclinical studies.Moreover, rapid proliferation is a distinguishing feature of peripheral tumour tissue, which can be attributed to the utilsation of adipose tissue or one-carbon units.By leveraging metabolic markers, 99 our objective is to identify specific metabolic characteristics linked to accelerated growth in peripheral tumours, offering valuable insights for guiding patients towards neoadjuvant therapy and optimising surgical procedures.For instance, patients exhibiting elevated lipid metabolism, may benefit from the complete removal of the adipose tissue surrounding the tumour and could potentially demonstrate heightened sensitivity to metabolic inhibitors that specifically target FA synthesis.Additionally, patients demonstrating heightened one-carbon metabolism may benefit from using inhibitors targeting one-carbon units.

Study limitations and future directions
Our investigation of the metabolomic and transcriptomic tissue heterogeneity within breast cancer is not without limitations.First of all, the cross-sectional design of our study provides only a static snapshot of tumour heterogeneity at a specific moment, limiting our understanding of its dynamic evolution.Longitudinal studies, tracking changes in glucose uptake rates and molecular profiles over time, are therefore essential for a more nuanced comprehension of intra-tumour heterogeneity.This is even more important as the division of tumours into central and peripheral regions was based on a single time point assessment of glucose uptake, overlooking potential temporal changes in spatial distribution and metabolic activity.That's why additional longitudinal assessments are imperative to capture the dynamic nature of tumour heterogeneity accurately.
Furthermore, our focus on transcriptomics and metabolomics analyses represents only a partial explo-ration of the full molecular omics landscape.The complexities of tumour biology extend to proteomics, epigenomics and immunopeptidomics, which were not addressed in our study.Integrating these additional layers of molecular analysis would help for a more comprehensive understanding of the intricate molecular variations within different tumour regions also towards their interaction with the immune system.Finally, novel technologies such as spatial metabolomics or ion mobility mass spectrometry would have helped to obtain a clearer picture upon metabolic pathways and in future studies we aim to integrate these techniques.
Moving forward, we envision leveraging the measurement of specific metabolic and gene markers associated with peripheral tumour growth and lipid metabolism.This strategic approach holds promise in guiding personalised decisions regarding neoadjuvant therapy and refining surgical protocols.By tailoring interventions based on the distinct metabolic signatures of individual tumours, we aspire to usher in a new era of precision medicine, optimising therapeutic outcomes and ultimately enhancing the quality of patient care in the realm of breast cancer management.

CONCLUSION
Our study provides a comprehensive framework for analysing the metabolic heterogeneity in human breast cancer by separating central and peripheral tumour tissues for imaging, transcriptomics and metabolomics studies, respectively.[ 18 F]FDG uptake was higher in the tumour centre, indicating a prevalence of the Warburg effect, while genetic analysis revealed enrichment of lipid metabolism pathways in the tumour periphery, this defines the metabolic heterogeneity.Metabolomic differences in LumA and LumB subtypes further demonstrate heterogeneity in breast cancer.Through the identification of marker differential metabolites between subtypes, such as acetate, serine and choline, we aim to enhance the discovery of potential metabolic tracer candidates for preclinical subtype differentiation.Measurement of specific metabolic and gene markers related to peripheral tumour growth and lipid metabolism has the potential to guide patients toward neoadjuvant therapy and optimised surgical protocols.

F I G U R E 1
The Consort diagram of samples collection.

F I G U R E 2
The genomic landscape of LumA and LumB based on public repository data of the TCGA database.(A) Principal component analysis (PCA) plot: the gene expression patterns of LumA and LumB tumours could not be distinguished clearly.(B) Kaplan-Meier curve: overall survival (OS) of patients with LumB tumours was lower than that of patients with LumA type.(C) Waterfall diagram: top 20 mutation genes in LumA, mutation of PIK3CA (53%), MAP3K1 (15%), KMT2C (13%), TP53 (6%), PTEN (5%), AKT1 (5%) were related to cancer TA B L E 2 Comparison of the central and periphery tumour by [ 18 F]FDG PET/MR in LumA and LumB (paired t-tests were used within regions of the same subtype and t-tests between different subtypes).

F I G U R E 4
Differential genes in tumour centre and periphery tumour areas.(A) Top 5 significantly different genes between the tumour centre and periphery in LumA tumours, involved in cell proliferation, genomic instability and poor prognosis.(B) Top 5 significantly different genes between the tumour centre and periphery in LumB tumours, involved in tumour oncogenes, cell cycle, DNA damage response, Wnt signalling and tumour angiogenesis.(C) Differential cell proliferation genes: Cell proliferation related genes were significantly higher in LumB periphery, and also increased in the LumA periphery, but not statistically significant.(D) Lipid metabolism-related genes: Lipid metabolism associated genes were significantly higher in peripheral of LumB tumours.The peripheral tumour of LumA tumours also showed an increase in these genes, but it was not significant (pair t-test) (*p < .05,**p < .01,***p < .001,****p < .0001).F I G U R E 5Metabolomics of the LumA and LumB tumours.(A and B) Orthogonal partial least-squares discrimination analysis (oPLS-DA) score plot: central and peripheral in LumA and LumB tumours illustrates centre comparable from periphery.(C and D) Principal component analysis (PCA) biplot illustrating the most important metabolites (acetate, formate and lactate) driving the separation of the principal components both in LumA and LumB tumours.(E and F) Volcano plot indicating statistically significant metabolites changes (G and H) Partial least squares-discriminant analysis (PLS-DA) identifies 15 metabolites with variable importance in projection (VIP) scores > 1 in LumA tumours and 16 metabolites with VIP scores > 1 in LumB tumours.Blue dotted line indicates the PLS-DA VIP score 1.0 threshold cut-off.(I and J) Deviation diagram: differences in peripheral and tumour metabolites in LumA and LumB tumours, blue shows increase in peripheral tumours, yellow shows increase in central tumours (Fold change > 1.2).LumB tumours has more different metabolites than LumA tumours.F I G U R E 6 Correlation of multi-omics results.(A) Correlation plot: the mean standardised uptake values (SUV) of [ 18 F]FDG correlated with metabolites, red points show positive correlation, blue points show negative correlation (FDR < .05,*).(B) Correlation plot: the gene set variation analysis (GSVA) scores of fatty acid metabolic processes correlated with metabolites, red points show positive correlation, blue points show negative correlation (FDR < .05,*).Arrows in correlation plots indicate metabolites correlation in LumA and LumB are opposite.
TA B L E 1